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1 TITLE: Metabolic Theory of Ecology and Stream Ecosystems FOLLOW

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1 TITLE: Metabolic Theory of Ecology and Stream Ecosystems FOLLOW Working Group Report – Post-ASM (2006) Metabolic Theory of Ecology and Stream Ecosystems TITLE : Metabolic Theory of Ecology and Stream Ecosystems FOLLOW-UP MEETING ORGANIZERS : Jennifer Follstad Shah, Emily Bernhardt, Alex Huryn MEETING DATES : May 2007 (meeting 1), summer 2008 (meeting 2) LOCATION : Sevilleta LTER Biological Field Station, New Mexico PARTICIPANTS PRESENT AT MEETING 1 (LTER site, affiliation): Faculty - Alex Huryn (ARC, Univ. of Alabama), Emily Bernhardt (CWT, Duke), James Brown (Univ. of New Mexico), Maury Valett (CWT, Virginia Tech), Pat Mulholland (CWT, Oak Ridge National Lab), Bob Sinsabaugh (SEV, Univ. of New Mexico), Bob Hall (Univ. of Wyoming), Bob Sterner (Univ. of Minnesota); Postdocs - Jennifer Follstad Shah (SEV, Duke), Brian Roberts (Oak Ridge National Lab 1), Krista Anderson (SEV, Univ. of New Mexico), Jim Hood (Univ. of Minnesota); Graduate student - Jordan Okie (Univ. of New Mexico) BACKGROUND : The recently formalized Metabolic Theory of Ecology (MTE) predicts how metabolic rate controls ecological processes at all scales (Brown et al. 2004). The MTE integrates first principles of thermodynamics, chemical kinetics, and physics to describe how metabolic processes are some function of body mass ( M), temperature ( T; in K), and the resource supply ( R) needed to fuel metabolism ( Y): b -E/kT Y = Y 0 M e f (R) (1) where Y0 is a normalization constant, b is an allometric exponent, E is the average activation energy of metabolism, and k is Boltzmann’s constant (8.62 ·10 -5 eV K -1). The MTE thus provides (1) a mechanistic model for understanding the complexity in nature and (2) testable predictions for a wide range of phenomena (including those related to core LTER research areas, such as population dynamics, primary production, and organic and inorganic matter processing). Though powerful, the MTE has limitations that constrain its application to ecological processes across ecosystems or landscapes. For example, the theory lacks a term that describes how resources (carbon, nitrogen, phosphorus, light) influence metabolic rate (Marquet et al. 2004). Also, most empirical tests of the MTE to date have used data from terrestrial ecosystems and often have mixed data from independent communities. Analyses presented at the LTER 2006 ASM Metabolic Theory of Ecology and Stream Ecosystems working group were among the first to test MTE predictions using data from stream ecosystems. We found that scaling exponents describing the relationship between production:biomass ratios and body mass for macroinvertebrates within three temperate stream communities were consistent with predicted ¼-power scaling relationships (Huryn and Benke 2007). However, residual variation of observed patterns was attributed to community-level differences in taxonomy and life history. We also found that the slope of the relationship between whole-stream respiration (g C m -2 d -1) and temperature (1/ kT, where k is the Boltzmann constant and T is temperature in K) across 15 studies in North America, Europe, and New Zealand was indistinguishable from that predicted by the MTE (-0.6 to -0.7). However, the slope of this relationship was positive rather than negative for data collected seasonally over two years within a single heterotrophic stream, indicating that whole-stream metabolism was greatest in fall and winter likely due to carbon inputs associated with leaf fall from adjacent forests. This result highlighted the need to better understand how resources interact with body mass and temperature to influence rates of metabolism across ecological scales. The follow-up working group proposed to synthesize numerous datasets to (1) more fully test the MTE and (2) incorporate resource supply into the MTE. FOLLOW-UP MEETING GOALS & SCOPE OF WORK : Primary goals of the follow-up working group included: (1) identification and synthesis of datasets from LTER and non-LTER sites; (2) tests of MTE predictions using these datasets; and (3) determination of how variation in resource supply may best be expressed as part of the MTE. 1 Current affiliation: Assistant Professor, Louisiana Universities Marine Consortium 1 Working Group Report – Post-ASM (2006) Metabolic Theory of Ecology and Stream Ecosystems Several questions were posed at the first working group meeting: 1. How do gradients or temporal fluctuations in resource supply alter scaling relationships between mass and metabolic rate or mass and population abundance? Are effects consistent across resource types? 2. Is resource supply temperature-dependent? If so, under which conditions? 3. How does taxonomic variation (e.g., trophic position, homeostatic regulation, up-regulation of N- or P- rich molecules, luxury consumption) alter mass- and temperature-dependent scaling relationships? 4. Does metabolic theory work in heterotrophic streams systems, in which carbon resources are donor-controlled (i.e., from adjacent forests) and occur in pulses during cooler temperatures? 5. Does metabolic theory work in non-equilibrium systems? Many of these questions were addressed at the first working group meeting through analyses conducted on four focus areas: 1. Bacterial production in the Ottawa, Maumee, and Hudson rivers 2. Stream macroinvertebrate secondary production and the Energy Equivalence Rule (EER) 3. Temperature-dependence of stream macroinvertebrate secondary production 4. Whole-stream rates of production and respiration Results for each focus area are described below, in addition to research efforts being conducted between working group meetings. The objectives of the second working group meeting are to (1) review results of each focus area, (2) finalize manuscript drafts, and (3) draft an outline for a research proposal for future LTER cross-site aquatic studies focused on MTE-related research questions that cannot be addressed using existing datasets. RESULTS : Focus area #1: Bacterial production in the Ottawa, Maumee, and Hudson rivers We re-examined data on annual cycles of bacterial production in the Ottawa, Maumee, and Hudson rivers. The data were originally reported in Sinsabaugh et al. (1997). We employed the MTE to isolate the effect of temperature on the activities of six extracellular enzymes (acetyl esterase, endopeptidase, leucyl aminopeptdase, alkaline phosphatase, alpha and beta glucosidase) used by riverine bacteria to obtain resources. We transformed enzyme activities measured in the lab at a standardized temperature (20 ºC) to activity rates for ambient stream temperatures using the mean of enzyme activation energies (0.5 eV) that have been reported in scientific literature. We then applied a modified Michaelis-Menten function to the transformed data to calculate a turnover rate (h -1) for each enzymatic substrate pool as App App Vmax /2 · Km (2) App App where Vmax is the maximum enzymatic activity rate, or catalytic capacity, and Km is the half App saturation constant. Vmax is a measure of the rate of resource consumption by riverine bacteria, while App Km is a measure of resource supply in the environment. The superscript “App” stands for “apparent”, since true measures of V max and K m are possible only under controlled lab settings. Equation 2 represents the affinity of an organism for a resource substrate. 2 Working Group Report – Post-ASM (2006) Metabolic Theory of Ecology and Stream Ecosystems 11 We found that bacterial production y = 0.855x - 1.9852 10 was not correlated with resource R2 = 0.5306 App 9 supply ( Km), but significant correlations existed between 8 App 7 AE maximum enzymatic activity ( Vmax ) ) EP 6 and bacterial production, especially max LAP V for glucosidase activity (a measure of 5 AG App App App 4 BG C use). Vmax and Km did not AP correlate within resources pools (i.e., LN( 3 for individual enzymes), but were well 2 2 correlated (r = 0.53) across multiple 1 resource pools over annual time scales 0 (Fig.1). The slope of the regression -1 was <1, indicating enzyme supply 1 2 3 4 5 6 7 8 9 10 11 12 App tends to trail increases in the size of LN( Km ) substrate pools. App -1 -1 Figure 1. Across resource pools, Vmax (nmol h L ), a measure of App Organisms consume multiple catalytic capacity, scales with Km (nM), a measure of substrate concentration. Enzyme abbreviations are as follows: AE = Acetyl- resources simultaneously, which esterase, EP = Endoprotease “trypsin”, LAP = Leucyl- presents a challenge for integrating aminopeptidase, AG = α-1,4-glucosidase, BG = β-1,4-glucosidase, AP resource supply into the MTE. A key = Alkaline phosphatase. Regression statistics: n = 317, F = 338, slope finding of our research was the SE = 0.047, slope 95% CI = 0.765 - 0.949. realization that utilization of multiple resources can be integrated App using the turnover rate ( Vmax /2 11 App Km) as a common metric, which normalizes enzyme activities that 10 vary over many orders of 9 magnitude (Fig. 2). Turnover rates 8 AE summed across all six extracellular EP enzymes were significantly correlated LAP 7 with bacterial production (r 2 = 0.56, n BP AG BG 6 = 39, F = 47, p <0.0001), indicating a AP good relationship between total consumption of resources and 5 bacterial production. 4 We are now using these 3 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 relationships to explore seasonal App App patterns in bacterial resource use. LN ( Vmax/2 Km Our objectives are to determine (1) Figure 2. Bacterial production (BP, nmol h -1 L -1) in relation to resource whether resource supply is -1 App App supply (h ), expressed as Vmax /2 Km, for substrate pools linked to the dependent on temperature
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